HS project
Figures
Audrey
2025-04-17
This file is used to prepare the figures for the paper.
library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)
.libPaths()## [1] "/usr/local/lib/R/library"Preparation
Here are the folders where analyses are stored :
data_dir = "./.."
list.files(data_dir)## [1] "0_intro" "1_metadata" "2_individual" "3_combined" "4_zoom"
## [6] "5_wu" "6_takahashi" "7_figures" "LICENSE" "README.md"
## [11] "index.html" "index_layout" "knit_all.sh"These are the custom colors for cell populations :
color_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_color_markers.rds"))
color_markers = color_markers[names(color_markers) != "melanocytes"] # remove melanocytes
data.frame(cell_type = names(color_markers),
color = unlist(color_markers)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(angle = 30, hjust = 1))
We define custom colors for sample type :
sample_type_colors = setNames(nm = c("HS", "HD"),
c("#C55F40", "#2C78E6"))
data.frame(cell_type = names(sample_type_colors),
color = unlist(sample_type_colors)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(sample_type_colors), breaks = names(sample_type_colors)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank())
We define custom colors for laboratory :
laboratory_colors = setNames(nm = c("Our", "Wu", "Takahashi"),
c("firebrick3", "deepskyblue", "mediumpurple"))
data.frame(cell_type = names(laboratory_colors),
color = unlist(laboratory_colors)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(laboratory_colors), breaks = names(laboratory_colors)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank())
We define custom colors for location :
location_colors = setNames(nm = c("axillary", "hair scalp", "pubis"),
c("forestgreen", "orchid3", "darkorange1"))
data.frame(cell_type = names(location_colors),
color = unlist(location_colors)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(location_colors), breaks = names(location_colors)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank())
We define custom colors for gender :
gender_colors = setNames(nm = c("F", "M"),
c("lightslateblue", "chartreuse3"))
data.frame(cell_type = names(gender_colors),
color = unlist(gender_colors)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(gender_colors), breaks = names(gender_colors)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank())
We set a background color :
bg_color = "gray94"This is the correspondence between cell types and cell families, and custom colors to color cells by cell category :
custom_order_cell_type = data.frame(
cell_type = names(color_markers),
cell_category = c(rep("immune cells", 5),
rep("matrix", 5),
rep("non matrix", 5)),
stringsAsFactors = FALSE)
custom_order_cell_type$cell_type = factor(custom_order_cell_type$cell_type,
levels = custom_order_cell_type$cell_type)
rownames(custom_order_cell_type) = custom_order_cell_type$cell_type
custom_order_cell_type## cell_type cell_category
## CD4 T cells CD4 T cells immune cells
## CD8 T cells CD8 T cells immune cells
## Langerhans cells Langerhans cells immune cells
## macrophages macrophages immune cells
## B cells B cells immune cells
## cuticle cuticle matrix
## cortex cortex matrix
## medulla medulla matrix
## IRS IRS matrix
## proliferative proliferative matrix
## HF-SCs HF-SCs non matrix
## IFE basal IFE basal non matrix
## IFE granular spinous IFE granular spinous non matrix
## ORS ORS non matrix
## sebocytes sebocytes non matrixcategory_color = c("immune cells" = "slateblue1",
"matrix" = "mediumseagreen",
"non matrix" = "firebrick3")We define markers to display on a dotplot to assess cell type annotation :
dotplot_markers = c("PTPRC", # immune cells
"CD3E", "CD4", # CD4+ T cells
"CD3E", "CD8A", # CD8+ T cells
"CD207", "AIF1", # Langerhans cells
"TREM2", "MSR1", # macrophages
"CD79A", "CD79B", # B cells
"MSX2", # matrix cells
"KRT32", "KRT35", # cuticle
"KRT31", "PRR9", # cortex
"BAMBI", "ALDH1A3", # medulla
"KRT71", "KRT73", # IRS
"TOP2A", "MCM5", "TK1", # cycling cells
"KRT14", "CXCL14", # non-matrix cells
"DIO2", "TCEAL2", # HF-SCs
"KRT15", "COL17A1", # IFE basal
"SPINK5", "KRT1", # ORS
"KRT16", "KRT6C", # IFE granular or spinous
"CLMP", "PPARG") # sebocytesWe copy-paste a subset of the markers associated with cell type annotation :
cell_markers = list(
# Immune cells
"CD4 T cells" = c("CD3D", "CD3G", "CD3E", "CD52", "IL32",
"TRBC1", "CD4", "COTL1", "CD40LG", "GPR183", "TNFRSF1B", "FYB1", "LAT"),
"CD8 T cells" = c("CD3D", "CD3G", "CD3E", "CD52", "IL32",
"CTSW", "NCR3", "CD8A", "NKG7", "KLRC1", "TRGC2", "CCL5", "ZNF683"),
"Langerhans cells" = c("CD207", "LST1", "AIF1", "CPVL", "C15orf48", "CD1A", "CD52", "CD1E", "CD1C"),
"macrophages" = c("LST1", "AIF1", "C3", "OLFML3", "TREM2", "A2M",
"MSR1", "FCGR3A", "VSIG4", "AP003481.1"),
"B cells" = c("CD79A", "IGHG2", "IGHG3", "IGLC3", "IGHG1", "IGHM", "MS4A1", "IGLC2",
"IGHGP", "BANK1", "IGHG4", "IGKC", "TNFRSF13C", "CD79B", "KLF2", "GZMB"),
# Matrix
"cuticle" = c("KRT32", "CYSTM1", "MT4", "KRT35", "VSNL1", "NOTCH1"),
"cortex" = c("C10orf99", "CRYAB", "HEPHL1", "ALOX15B", "EFHD1", "DSG4", "DNASE1L2", "TGM3", "KRT31"),
"medulla" = c("BAMBI", "SLC7A8", "EDNRA", "IGFBP2", "KRT25", "KITLG"),
"IRS" = c("KRT71", "KRT27", "KRT28", "CTSC", "GATA3", "KRT73", "SCEL"),
"proliferative" = c("TOP2A", "MKI67", "BIRC5", "TK1", "MCM5"),
# Non-matrix
"HF-SCs" = c("KRT15", "DST", "DIO2", "TCEAL2", "SFRP1", "TNC", "WIF1", "FRZB", "LHX2", "COL17A1"),
"IFE basal" = c("GPX2", "MOXD1", "C19orf48", "ALDH3A1", "CDH13", "LAMB3", "CAVIN1"),
"IFE granular spinous" = c("DEFB1", "LY6D", "EHF", "PDZK1IP1", "S100A7", "FGFBP1", "SPINK5", "KRT1", "KRTDAP"),
"ORS" = c("KRT16", "KRT6B", "KRT6C", "TM4SF1", "APOC1", "CBLN2", "HES4", "LYPD3"),
# other
"melanocytes" = c("DCT", "KIT", "MLANA", "GPM6B", "EDNRB", "PMEL", "IGFBP7", "MITF", "ZEB2", "APOD"),
"sebocytes" = c("CLMP", "PPARG", "ACSBG1", "MGST1", "SAA1", "APMAP"))
lengths(cell_markers)## CD4 T cells CD8 T cells Langerhans cells
## 13 13 9
## macrophages B cells cuticle
## 10 16 6
## cortex medulla IRS
## 9 6 7
## proliferative HF-SCs IFE basal
## 5 10 7
## IFE granular spinous ORS melanocytes
## 9 8 10
## sebocytes
## 6Custom functions to display gene expression on the heatmap :
color_fun = function(one_gene) {
gene_range = range(ht_annot[, one_gene])
gene_palette = circlize::colorRamp2(colors = c("#FFFFFF", aquarius::color_gene[-1]),
breaks = seq(from = gene_range[1], to = gene_range[2],
length.out = length(aquarius::color_gene)))
return(gene_palette)
}Load datasets
In a list, we load:
- Seurat object
- name of the 2D projection to visualize cells on
- various third element such as: sample information, trajectory, results from gene analysis…
list_data = list()
## Our dataset
# Atlas of all cells
list_data$our_atlas$sobj = readRDS(paste0(data_dir, "/3_combined/hs_hd_sobj.rds"))
list_data$our_atlas$name2D = "harmony_38_tsne"
list_data$our_atlas$sample_info = readRDS(paste0(data_dir, "/1_metadata/hs_hd_sample_info.rds"))
list_data$our_atlas$sobj## An object of class Seurat
## 20003 features across 12111 samples within 1 assay
## Active assay: RNA (20003 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne# ORS + IFEb dataset
list_data$ors_ifeb$sobj = readRDS(paste0(data_dir, "/4_zoom/3_zoom_ors_ifeb/ors_ifeb_sobj.rds"))
list_data$ors_ifeb$name2D = "harmony_20_tsne"
list_data$ors_ifeb$list_results = readRDS(paste0(data_dir, "/4_zoom/3_zoom_ors_ifeb/ors_ifeb_list_results.rds"))
list_data$ors_ifeb$sobj## An object of class Seurat
## 16683 features across 3416 samples within 1 assay
## Active assay: RNA (16683 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne# HF-SCs dataset
list_data$hfsc$sobj = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_sobj.rds"))
list_data$hfsc$name2D = "harmony_24_tsne"
list_data$hfsc$list_results = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_list_results.rds"))
list_data$hfsc$sobj## An object of class Seurat
## 15384 features across 1454 samples within 1 assay
## Active assay: RNA (15384 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_24_tsne, RNA_pca_24_umap, harmony, harmony_24_umap, harmony_24_tsne# Non matrix cells dataset
list_data$non_matrix$sobj = readRDS(paste0(data_dir, "/4_zoom/5_zoom_non_matrix/non_matrix_sobj_traj_tinga.rds"))
list_data$non_matrix$name2D = "harmony_dm"
list_data$non_matrix$my_traj = readRDS(paste0(data_dir, "/4_zoom/5_zoom_non_matrix/non_matrix_my_traj_tinga.rds"))
list_data$non_matrix$sobj## An object of class Seurat
## 17837 features across 5599 samples within 1 assay
## Active assay: RNA (17837 features, 2000 variable features)
## 8 dimensional reductions calculated: RNA_pca, RNA_pca_18_tsne, RNA_pca_18_umap, harmony, harmony_18_umap, harmony_18_tsne, harmony_dm, harmony_dm_5_umap# Matrix cells dataset (not considered in the manuscript)
# list_data$matrix$sobj = readRDS(paste0(data_dir, "/4_zoom/6_zoom_matrix/matrix_sobj.rds"))
# list_data$matrix$name2D = "harmony_19_tsne"
# list_data$matrix$list_results = readRDS(paste0(data_dir, "/4_zoom/6_zoom_matrix/matrix_list_results.rds"))
# list_data$matrix$sobj
# Immune cells
list_data$immune$sobj = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_sobj.rds"))
list_data$immune$name2D = "harmony_20_tsne"
list_data$immune$list_results = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_list_results.rds"))
list_data$immune$sobj## An object of class Seurat
## 15121 features across 2329 samples within 1 assay
## Active assay: RNA (15121 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne## Wu dataset (OEP002321)
# Atlas of all cells
list_data$wu_atlas$sobj = readRDS(paste0(data_dir, "/5_wu/3_combined/wu_sobj.rds"))
list_data$wu_atlas$name2D = "harmony_39_tsne"
list_data$wu_atlas$sample_info = readRDS(paste0(data_dir, "/5_wu/1_metadata/wu_sample_info.rds"))
# ORS + IFEb dataset
list_data$wu_ors_ifeb$sobj = readRDS(paste0(data_dir, "/5_wu/4_ors_ifeb/ors_ifeb_sobj.rds"))
list_data$wu_ors_ifeb$name2D = "harmony_20_tsne"
list_data$wu_ors_ifeb$list_results = readRDS(paste0(data_dir, "/5_wu/4_ors_ifeb/ors_ifeb_list_results.rds"))
list_data$wu_ors_ifeb$sobj## An object of class Seurat
## 15543 features across 7987 samples within 1 assay
## Active assay: RNA (15543 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_19_tsne, RNA_pca_19_umap, harmony, harmony_19_umap, harmony_19_tsne## Takahashi dataset (GSE129611)
# Atlas of all cells
list_data$takahashi_atlas$sobj = readRDS(paste0(data_dir, "/6_takahashi/3_combined/takahashi_sobj.rds"))
list_data$takahashi_atlas$name2D = "harmony_41_tsne"
list_data$takahashi_atlas$sample_info = readRDS(paste0(data_dir, "/6_takahashi/1_metadata/takahashi_sample_info.rds"))
list_data$takahashi_atlas$sobj## An object of class Seurat
## 18588 features across 5636 samples within 1 assay
## Active assay: RNA (18588 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_41_tsne, RNA_pca_41_umap, harmony, harmony_41_umap, harmony_41_tsne# ORS + IFEb dataset
list_data$takahashi_ors_ifeb$sobj = readRDS(paste0(data_dir, "/6_takahashi/4_ors_ifeb/ors_ifeb_sobj.rds"))
list_data$takahashi_ors_ifeb$name2D = "harmony_23_tsne"
list_data$takahashi_ors_ifeb$list_results = readRDS(paste0(data_dir, "/6_takahashi/4_ors_ifeb/ors_ifeb_list_results.rds"))
list_data$takahashi_ors_ifeb$sobj## An object of class Seurat
## 14911 features across 1562 samples within 1 assay
## Active assay: RNA (14911 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_23_tsne, RNA_pca_23_umap, harmony, harmony_23_umap, harmony_23_tsne## Three datasets combined (Our + OEP002321 + GSE129611)
list_data$combined$sobj = readRDS(paste0(data_dir, "/3_combined/data3_sobj.rds"))
list_data$combined$name2D = "harmony_37_tsne"
list_data$combined$sobj## An object of class Seurat
## 19849 features across 35289 samples within 1 assay
## Active assay: RNA (19849 features, 2000 variable features)
## 6 dimensional reductions calculated: RNA_pca, RNA_pca_37_tsne, RNA_pca_37_umap, harmony, harmony_37_umap, harmony_37_tsne## Print
str(list_data, max.level = 2)## List of 10
## $ our_atlas :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_38_tsne"
## ..$ sample_info:'data.frame': 7 obs. of 8 variables:
## $ ors_ifeb :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_20_tsne"
## ..$ list_results:List of 4
## $ hfsc :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_24_tsne"
## ..$ list_results:List of 11
## $ non_matrix :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_dm"
## ..$ my_traj:List of 18
## .. ..- attr(*, "class")= chr [1:4] "dynwrap::with_dimred" "dynwrap::with_trajectory" "dynwrap::data_wrapper" "list"
## $ immune :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_20_tsne"
## ..$ list_results:List of 4
## $ wu_atlas :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_39_tsne"
## ..$ sample_info:'data.frame': 6 obs. of 8 variables:
## $ wu_ors_ifeb :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_20_tsne"
## ..$ list_results:List of 1
## $ takahashi_atlas :List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_41_tsne"
## ..$ sample_info:'data.frame': 5 obs. of 8 variables:
## $ takahashi_ors_ifeb:List of 3
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D : chr "harmony_23_tsne"
## ..$ list_results:List of 1
## $ combined :List of 2
## ..$ sobj :Formal class 'Seurat' [package "Seurat"] with 12 slots
## ..$ name2D: chr "harmony_37_tsne"Figures
Sample information
Our dataset:
sobj = list_data$our_atlas$sobj
sample_info = list_data$our_atlas$sample_info
# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
as.data.frame.table(., stringsAsFactors = FALSE) %>%
`colnames<-`(c("sample_identifier", "nb_cells")) %>%
dplyr::left_join(x = ., y = sample_info, by = "sample_identifier")
# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
Wu dataset:
sobj = list_data$wu_atlas$sobj
sample_info = list_data$wu_atlas$sample_info
# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
as.data.frame.table(., stringsAsFactors = FALSE) %>%
`colnames<-`(c("sample_identifier", "nb_cells")) %>%
dplyr::left_join(x = ., y = sample_info, by = "sample_identifier")
# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
Takahashi dataset:
sobj = list_data$takahashi_atlas$sobj
sample_info = list_data$takahashi_atlas$sample_info
# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
as.data.frame.table(., stringsAsFactors = FALSE) %>%
`colnames<-`(c("sample_identifier", "nb_cells")) %>%
dplyr::left_join(x = ., y = sample_info, by = "sample_identifier")
# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
Annotation smoothing
For each dataset, we smooth the single-cell level cell type annotation at a cluster level.
- Our dataset:
# Select dataset
sobj = list_data$our_atlas$sobj
# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
as.character() %>%
factor(., levels = names(color_markers))
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
prop.table(., margin = 2) %>%
apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
levels(sobj$cell_type)[cluster_type])
sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
levels = levels(sobj$cell_type))
# Consider change
list_data$our_atlas$sobj = sobj# Select dataset
sobj = list_data$non_matrix$sobj
# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
as.character() %>%
factor(., levels = names(color_markers))
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
prop.table(., margin = 2) %>%
apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
levels(sobj$cell_type)[cluster_type])
sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
levels = levels(sobj$cell_type))
# Consider change
list_data$non_matrix$sobj = sobj# Select dataset
sobj = list_data$immune$sobj
# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
as.character() %>%
factor(., levels = names(color_markers))
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
prop.table(., margin = 2) %>%
apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
levels(sobj$cell_type)[cluster_type])
sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
levels = levels(sobj$cell_type))
# Consider change
list_data$immune$sobj = sobj- Wu dataset:
# Select dataset
sobj = list_data$wu_atlas$sobj
# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
as.character() %>%
factor(., levels = names(color_markers))
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
prop.table(., margin = 2) %>%
apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
levels(sobj$cell_type)[cluster_type])
sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
levels = levels(sobj$cell_type))
# Consider change
list_data$wu_atlas$sobj = sobj- Takahashi dataset:
# Select dataset
sobj = list_data$takahashi_atlas$sobj
# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
as.character() %>%
factor(., levels = names(color_markers))
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
prop.table(., margin = 2) %>%
apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
levels(sobj$cell_type)[cluster_type])
sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
levels = levels(sobj$cell_type))
# Consider change
list_data$takahashi_atlas$sobj = sobj- Combined dataset:
# Select dataset
sobj = list_data$combined$sobj
# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
as.character() %>%
factor(., levels = names(color_markers))
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
prop.table(., margin = 2) %>%
apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
levels(sobj$cell_type)[cluster_type])
sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
levels = levels(sobj$cell_type))
# Consider change
list_data$combined$sobj = sobjFor the combined dataset, we also define the cell category:
# Select dataset
sobj = list_data$combined$sobj
# Define cell category
sobj$cell_category = custom_order_cell_type[sobj$cell_type, "cell_category"]
table(sobj$cell_type, sobj$cell_category)##
## immune cells matrix non matrix
## CD4 T cells 1059 0 0
## CD8 T cells 652 0 0
## Langerhans cells 461 0 0
## macrophages 571 0 0
## B cells 207 0 0
## cuticle 0 3021 0
## cortex 0 2163 0
## medulla 0 903 0
## IRS 0 1152 0
## proliferative 0 2590 0
## HF-SCs 0 0 3292
## IFE basal 0 0 4117
## IFE granular spinous 0 0 5349
## ORS 0 0 9121
## sebocytes 0 0 631# Consider change
list_data$combined$sobj = sobjAtlas
Our dataset
We select the dataset:
sobj = list_data$our_atlas$sobj
name2D = list_data$our_atlas$name2D
sample_info = list_data$our_atlas$sample_info- sample identifier
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))
# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
as.data.frame() %>%
`colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_identifier = sobj$sample_identifier
cells_coord$location = sobj$location
cells_coord = cells_coord[(rnd_order), ]
# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_identifier)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = sample_info$color,
breaks = sample_info$sample_identifier) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- location
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = location)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = location_colors,
breaks = names(location_colors)) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- cell type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cell_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cluster type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cluster_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- dotplot for cell type annotation
plot_list = aquarius::plot_dotplot(sobj,
markers = dotplot_markers,
assay = "RNA", column_name = "cell_type", nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(legend.position = "left",
legend.justification = "bottom",
legend.box = "vertical",
legend.box.margin = margin(0,70,0,0),
axis.title = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
plot.margin = unit(rep(0, 4), "cm"))
p = data.frame(cell_type = factor(levels(sobj$cell_type),
levels = levels(sobj$cell_type))) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0)) +
ggplot2::geom_point(size = 0) +
ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = category_color["immune cells"]) +
ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = category_color["matrix"]) +
ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = category_color["non matrix"]) +
ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
axis.line.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
plot.margin = unit(c(0,0.5,0.5,0), "cm"))
plot_list = patchwork::wrap_plots(plot_list, p,
ncol = 1, heights = c(25, 1))
plot_list
- barplot with proportion of cells for cell types of interest, by sample ID
# Cells of interest
cells_oi = c("CD4 T cells", "macrophages")
# Proportion of cells by sample ID
df_proportion = table(sobj$cluster_type,
sobj$sample_identifier) %>%
prop.table(., margin = 2) %>%
as.data.frame.table() %>%
`colnames<-`(c("cluster_type", "sample_identifier", "freq")) %>%
dplyr::filter(cluster_type %in% cells_oi) %>%
dplyr::mutate(cluster_type = factor(cluster_type, levels = cells_oi)) %>%
dplyr::mutate(freq = 100*freq) # percentage
# Plot
ggplot2::ggplot(df_proportion, aes(x = sample_identifier,
y = freq,
fill = cluster_type)) +
ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge(),
color = "black", width = 0.7) +
ggplot2::labs(y = "% of cells by sample") +
ggplot2::scale_y_continuous(expand = c(0, 0), limits = c(0,14)) +
ggplot2::scale_fill_manual(values = color_markers[cells_oi],
breaks = cells_oi,
name = "Cell Type") +
ggplot2::theme_classic() +
ggplot2::theme(axis.title.x = element_blank(),
axis.line.x = element_line(color = "gray50"),
panel.grid.major.y = element_line(),
panel.grid.minor.y = element_line(),
text = element_text(size = 15))
Wu dataset
We select the dataset:
sobj = list_data$wu_atlas$sobj
name2D = list_data$wu_atlas$name2D
sample_info = list_data$wu_atlas$sample_info- sample identifier
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))
# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
as.data.frame() %>%
`colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_identifier = sobj$sample_identifier
cells_coord = cells_coord[(rnd_order), ]
# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_identifier)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = sample_info$color,
breaks = sample_info$sample_identifier) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- cell type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cell_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cluster type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cluster_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- dotplot for cell type annotation
plot_list = aquarius::plot_dotplot(sobj,
markers = dotplot_markers,
assay = "RNA", column_name = "cell_type", nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(legend.position = "left",
legend.justification = "bottom",
legend.box = "vertical",
legend.box.margin = margin(0,70,0,0),
axis.title = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
plot.margin = unit(rep(0, 4), "cm"))
p = data.frame(cell_type = factor(levels(sobj$cell_type),
levels = levels(sobj$cell_type))) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0)) +
ggplot2::geom_point(size = 0) +
ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = category_color["immune cells"]) +
ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = category_color["matrix"]) +
ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = category_color["non matrix"]) +
ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
axis.line.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
plot.margin = unit(c(0,0.5,0.5,0), "cm"))
plot_list = patchwork::wrap_plots(plot_list, p,
ncol = 1, heights = c(25, 1))
plot_list
Takahashi dataset
We select the dataset:
sobj = list_data$takahashi_atlas$sobj
name2D = list_data$takahashi_atlas$name2D
sample_info = list_data$takahashi_atlas$sample_info- sample identifier
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))
# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
as.data.frame() %>%
`colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_identifier = sobj$sample_identifier
cells_coord = cells_coord[(rnd_order), ]
# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_identifier)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = sample_info$color,
breaks = sample_info$sample_identifier) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- cell type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cell_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cluster type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cluster_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- dotplot for cell type annotation
plot_list = aquarius::plot_dotplot(sobj,
markers = dotplot_markers,
assay = "RNA", column_name = "cell_type", nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(legend.position = "left",
legend.justification = "bottom",
legend.box = "vertical",
legend.box.margin = margin(0,70,0,0),
axis.title = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
plot.margin = unit(rep(0, 4), "cm"))
p = data.frame(cell_type = factor(levels(sobj$cell_type),
levels = levels(sobj$cell_type))) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0)) +
ggplot2::geom_point(size = 0) +
ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = category_color["immune cells"]) +
ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = category_color["matrix"]) +
ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = category_color["non matrix"]) +
ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title = element_blank(),
axis.line.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
plot.margin = unit(c(0,0.5,0.5,0), "cm"))
plot_list = patchwork::wrap_plots(plot_list, p,
ncol = 1, heights = c(25, 1))
plot_list
Combined datasets
We select the dataset:
sobj = list_data$combined$sobj
name2D = list_data$combined$name2D
sample_info = rbind(list_data$our_atlas$sample_info,
list_data$wu_atlas$sample_info,
list_data$takahashi_atlas$sample_info)- sample identifier
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))
# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
as.data.frame() %>%
`colnames<-`(c("Dim1", "Dim2"))
cells_coord$sample_identifier = sobj$sample_identifier
cells_coord$location = sobj$location
cells_coord$laboratory = sobj$laboratory
cells_coord$gender = dplyr::left_join(sobj@meta.data,
sample_info,
by = "project_name")[, "gender"]
cells_coord = cells_coord[(rnd_order), ]
# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = sample_identifier)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = sample_info$color,
breaks = sample_info$sample_identifier) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- location
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = location)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = location_colors,
breaks = names(location_colors)) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- laboratory
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = laboratory)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = laboratory_colors,
breaks = names(laboratory_colors)) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- gender
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = gender)) +
ggplot2::geom_point(size = 0.5) +
ggplot2::scale_color_manual(values = gender_colors,
breaks = names(gender_colors)) +
ggplot2::theme_void() +
ggplot2::theme(aspect.ratio = 1,
legend.position = "none")
- cell type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cell_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cluster type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cluster_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- split by laboratory:
plot_list = aquarius::plot_split_dimred(sobj,
reduction = name2D,
split_by = "laboratory",
group_by = "cluster_type",
group_color = color_markers,
order = TRUE,
bg_color = bg_color)
patchwork::wrap_plots(plot_list, nrow = 1) +
patchwork::plot_layout(guides = "collect") &
Seurat::NoLegend()
- gene to assess global annotation
plot_list = lapply(c("PTPRC", "MSX2", "KRT14"), FUN = function(one_gene) {
p = Seurat::FeaturePlot(sobj, features = one_gene, reduction = name2D) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
return(p)
})
plot_list## [[1]]
##
## [[2]]
##
## [[3]]
- violin plot, split by cell type, split by laboratory
for non matrix cells
category_of_interest = "non matrix"
# Select cells of interest
cell_type_of_interest = as.character(custom_order_cell_type[
which(custom_order_cell_type$cell_category == category_of_interest), "cell_type"])
subsobj_of_interest = subset(sobj, cell_type %in% cell_type_of_interest)
subsobj_of_interest$cell_type_labo = paste0(subsobj_of_interest$cell_type,
subsobj_of_interest$laboratory)
cell_markers_of_interest = cell_markers[cell_type_of_interest]
total_height = max(lengths(cell_markers_of_interest)) + 1
# Violin plot
patchwork_list = lapply(names(cell_markers_of_interest), FUN = function(cell_type) {
markers_set = cell_markers_of_interest[[cell_type]]
# Individual violin plot
sub_plot_list = lapply(markers_set, FUN = function(one_gene) {
p = Seurat::VlnPlot(subsobj_of_interest,
features = one_gene, pt.size = 0,
group.by = "cell_type",
split.by = "laboratory",
cols = laboratory_colors,
combine = FALSE)[[1]] +
ggplot2::labs(y = one_gene) +
ggplot2::scale_y_continuous(expand = c(0.01, 0)) +
ggplot2::geom_vline(mapping = NULL, data = NULL,
xintercept = c(1:4) + 0.5,
col = "gray50", lty = 2) +
Seurat::NoLegend() +
ggplot2::theme(plot.title = element_blank(),
axis.title.x = element_blank(), # "Identity"
axis.line.x = element_blank(), # replaced by panel.border
axis.title.y = element_text(size = 8, angle = 0, vjust = 0.5), # gene
axis.text.y = element_blank(), # "Expression level"
axis.text.x = element_blank(), # cell type
axis.ticks = element_blank(),
plot.margin = unit(c(0,0,0,0), "cm"),
panel.border = element_rect(size = 1, color = "gray50"))
return(p)
})
# Reset axis.text for last plot
# sub_plot_list[[length(sub_plot_list)]] = sub_plot_list[[length(sub_plot_list)]] +
# ggplot2::theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) # cell type
# Add empty plot to arrange heights
sub_plot_list[[length(sub_plot_list) + 1]] = patchwork::plot_spacer()
empty_plot_height = total_height - length(markers_set)
# Arrange as patchwork
pp = patchwork::wrap_plots(sub_plot_list,
ncol = 1,
heights = c(rep(1, length(markers_set)),
empty_plot_height)) +
patchwork::plot_annotation(title = cell_type)
return(pp)
})
names(patchwork_list) = names(cell_markers_of_interest)
patchwork::wrap_plots(patchwork_list, ncol = 1)
for matrix cells
category_of_interest = "matrix"
# Select cells of interest
cell_type_of_interest = as.character(custom_order_cell_type[
which(custom_order_cell_type$cell_category == category_of_interest), "cell_type"])
subsobj_of_interest = subset(sobj, cell_type %in% cell_type_of_interest)
subsobj_of_interest$cell_type_labo = paste0(subsobj_of_interest$cell_type,
subsobj_of_interest$laboratory)
cell_markers_of_interest = cell_markers[cell_type_of_interest]
total_height = max(lengths(cell_markers_of_interest)) + 1
# Violin plot
patchwork_list = lapply(names(cell_markers_of_interest), FUN = function(cell_type) {
markers_set = cell_markers_of_interest[[cell_type]]
# Individual violin plot
sub_plot_list = lapply(markers_set, FUN = function(one_gene) {
p = Seurat::VlnPlot(subsobj_of_interest,
features = one_gene, pt.size = 0,
group.by = "cell_type",
split.by = "laboratory",
cols = laboratory_colors,
combine = FALSE)[[1]] +
ggplot2::labs(y = one_gene) +
ggplot2::scale_y_continuous(expand = c(0.01, 0)) +
ggplot2::geom_vline(mapping = NULL, data = NULL,
xintercept = c(1:4) + 0.5,
col = "gray50", lty = 2) +
Seurat::NoLegend() +
ggplot2::theme(plot.title = element_blank(),
axis.title.x = element_blank(), # "Identity"
axis.line.x = element_blank(), # replaced by panel.border
axis.title.y = element_text(size = 8, angle = 0, vjust = 0.5), # gene
axis.text.y = element_blank(), # "Expression level"
axis.text.x = element_blank(), # cell type
axis.ticks = element_blank(),
plot.margin = unit(c(0,0,0,0), "cm"),
panel.border = element_rect(size = 1, color = "gray50"))
return(p)
})
# Reset axis.text for last plot
# sub_plot_list[[length(sub_plot_list)]] = sub_plot_list[[length(sub_plot_list)]] +
# ggplot2::theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) # cell type
# Add empty plot to arrange heights
sub_plot_list[[length(sub_plot_list) + 1]] = patchwork::plot_spacer()
empty_plot_height = total_height - length(markers_set)
# Arrange as patchwork
pp = patchwork::wrap_plots(sub_plot_list,
ncol = 1,
heights = c(rep(1, length(markers_set)),
empty_plot_height)) +
patchwork::plot_annotation(title = cell_type)
return(pp)
})
names(patchwork_list) = names(cell_markers_of_interest)
patchwork::wrap_plots(patchwork_list, ncol = 1)
Non-matrix cells
We select the datasets:
sobj_atlas = list_data$our_atlas$sobj
name2D_atlas = list_data$our_atlas$name2D
sobj = list_data$non_matrix$sobj
name2D = list_data$non_matrix$name2D
my_traj = list_data$non_matrix$my_traj
cell_type_of_interest = c("HF-SCs", "ORS", "IFE basal", "IFE granular spinous")Location of cells on the whole atlas:
sobj_atlas$cell_bc = colnames(sobj_atlas)
sobj$cell_bc = colnames(sobj)
sobj_atlas$is_of_interest = dplyr::left_join(sobj_atlas@meta.data[, c("cell_bc", "percent.mt")],
sobj@meta.data[, c("cell_bc", "cluster_type")],
by = "cell_bc")[, "cluster_type"]
Seurat::DimPlot(sobj_atlas, reduction = name2D_atlas, pt.size = 0.000001,
group.by = "is_of_interest", order = "TRUE") +
ggplot2::scale_color_manual(values = c(color_markers[cell_type_of_interest], bg_color),
breaks = c(cell_type_of_interest, NA), na.value = bg_color) +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_blank()) +
Seurat::NoAxes() + Seurat::NoLegend()
- sample type
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "sample_type", cols = sample_type_colors) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cell type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cell_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cluster type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cluster_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- pseudotime
Seurat::FeaturePlot(sobj, reduction = name2D, pt.size = 0.5,
features = "pseudotime") +
ggplot2::scale_color_gradientn(colors = viridis::viridis(n = 100)) +
ggplot2::lims(x = range(sobj@reductions[[name2D]]@cell.embeddings[, 1]),
y = range(sobj@reductions[[name2D]]@cell.embeddings[, 2])) +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
Seurat::NoAxes()
- pseudotime with dynplot’s function :
dynplot::plot_dimred(trajectory = my_traj,
dimred = sobj[[name2D]]@cell.embeddings,
# Cells
color_cells = 'pseudotime',
size_cells = 0.5,
border_radius_percentage = 0,
# Trajectory
plot_trajectory = TRUE,
color_trajectory = "none",
label_milestones = FALSE,
size_milestones = 0,
size_transitions = 1)
- violin plots
features_oi = c("SOX9", "TCF4", "TBX1", "NFATC1",
"LHX2", "RUNX1", "VIM", "TCF3", "FGF18", "CD34",
# HF-SCs + IFE basal
"COL17A1", "MT2A", "TXNIP", "CAVIN1",
# HF-SCs + ORS
"TUBB2A", "KRT6B", "MARCKSL1", "ID4", "CYP1B1", "BARX2",
# HF-SCs
"LMCD1", "TCEAL2", "FRZB", "THBS1", "DIO2")
plot_list = lapply(features_oi, FUN = function(one_gene) {
p = Seurat::VlnPlot(sobj, group.by = "cluster_type",
features = one_gene, pt.size = 0.001) +
ggplot2::scale_fill_manual(values = color_markers[levels(sobj$cluster_type)],
breaks = levels(sobj$cluster_type)) +
ggplot2::theme(plot.subtitle = element_text(hjust = 0.5),
axis.title.x = element_blank(),
legend.position = "none")
return(p)
})
names(plot_list) = features_oi
plot_list## $SOX9
##
## $TCF4
##
## $TBX1
##
## $NFATC1
##
## $LHX2
##
## $RUNX1
##
## $VIM
##
## $TCF3
##
## $FGF18
##
## $CD34
##
## $COL17A1
##
## $MT2A
##
## $TXNIP
##
## $CAVIN1
##
## $TUBB2A
##
## $KRT6B
##
## $MARCKSL1
##
## $ID4
##
## $CYP1B1
##
## $BARX2
##
## $LMCD1
##
## $TCEAL2
##
## $FRZB
##
## $THBS1
##
## $DIO2
- feature plots
plot_list = lapply(features_oi, FUN = function(one_gene) {
p = Seurat::FeaturePlot(sobj, features = one_gene, reduction = name2D) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::lims(x = range(sobj@reductions[[name2D]]@cell.embeddings[, 1]),
y = range(sobj@reductions[[name2D]]@cell.embeddings[, 2])) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
return(p)
})
names(plot_list) = features_oi
plot_list## $SOX9
##
## $TCF4
##
## $TBX1
##
## $NFATC1
##
## $LHX2
##
## $RUNX1
##
## $VIM
##
## $TCF3
##
## $FGF18
##
## $CD34
##
## $COL17A1
##
## $MT2A
##
## $TXNIP
##
## $CAVIN1
##
## $TUBB2A
##
## $KRT6B
##
## $MARCKSL1
##
## $ID4
##
## $CYP1B1
##
## $BARX2
##
## $LMCD1
##
## $TCEAL2
##
## $FRZB
##
## $THBS1
##
## $DIO2
ORS vs IFE basal
Our dataset
We select the dataset:
sobj_atlas = list_data$our_atlas$sobj
name2D_atlas = list_data$our_atlas$name2D
sobj = list_data$ors_ifeb$sobj
name2D = list_data$ors_ifeb$name2D
list_results = list_data$ors_ifeb$list_resultsLocation of cells on the whole atlas:
sobj_atlas$cell_bc = colnames(sobj_atlas)
sobj$cell_bc = colnames(sobj)
sobj_atlas$is_of_interest = dplyr::left_join(sobj_atlas@meta.data[, c("cell_bc", "percent.mt")],
sobj@meta.data[, c("cell_bc", "cell_type")],
by = "cell_bc")[, "cell_type"]
Seurat::DimPlot(sobj_atlas, reduction = name2D_atlas, pt.size = 0.000001,
group.by = "is_of_interest", order = "TRUE") +
ggplot2::scale_color_manual(values = c(color_markers[cell_type_of_interest], bg_color),
breaks = c(cell_type_of_interest, NA), na.value = bg_color) +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_blank()) +
Seurat::NoAxes() + Seurat::NoLegend()
DE genes between ORS and IFE basal :
mark = list_results$ORS_vs_IFE_basal$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
# up-regulated in ORS
mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
# up-regulated in IFE basal
mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
# representative and selective for ORS
mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
# representative and selective for IFE basal
mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]
avg_logFC_range = setNames(c(min(mark_label$avg_logFC), -1, 0, 1, max(mark_label$avg_logFC)),
nm = c("dodgerblue4", "dodgerblue3", "#B7B7B7", "firebrick3", "firebrick4"))
ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
ggplot2::geom_point() +
ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
aes(x = pct.1, y = pct.2, label = gene_name),
col = "black", fill = NA, size = 3.5, label.size = NA) +
ggplot2::labs(x = "Enriched in ORS",
y = "Enriched in IFE basal") +
ggplot2::scale_color_gradientn(colors = names(avg_logFC_range),
values = scales::rescale(unname(avg_logFC_range))) +
ggplot2::theme_classic() +
ggplot2::theme(aspect.ratio = 1)
- GSEA, enriched in ORS compared to IFE basal
the_gs_name = "REACTOME_KERATINIZATION"
the_content = list_results$ORS_vs_IFE_basal$gsea@result %>%
dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
"\t|\tpvalue : ", round(the_content$pvalue, 4),
"\t|\tset size : ", the_content$setSize, " genes")
enrichplot::gseaplot2(x = list_results$ORS_vs_IFE_basal$gsea,
geneSetID = the_gs_name) +
ggplot2::labs(title = the_gs_name,
subtitle = the_subtitle) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
margin = ggplot2::margin(3, 3, 5, 3)),
plot.subtitle = ggtext::element_markdown(hjust = 0.5,
size = 10))
- GSEA, enriched in IFE basal compared to ORS
the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE"
the_content = list_results$ORS_vs_IFE_basal$gsea@result %>%
dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
"\t|\tpvalue : ", round(the_content$pvalue, 4),
"\t|\tset size : ", the_content$setSize, " genes")
enrichplot::gseaplot2(x = list_results$ORS_vs_IFE_basal$gsea,
geneSetID = the_gs_name) +
ggplot2::labs(title = the_gs_name,
subtitle = the_subtitle) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
margin = ggplot2::margin(3, 3, 5, 3)),
plot.subtitle = ggtext::element_markdown(hjust = 0.5,
size = 10))
- violin plots of DE genes in ORS, between HS and HD
subsobj = subset(sobj, cell_type == "ORS")
features_oi = c("DUSP1", "DDIT4", "GABARAP", "ZNF302",
"MIF", "LGALS7", "ARF5", "S100A9")
# Plot !
plot_list = lapply(features_oi, FUN = function(feature) {
# t-test between sample type
feature_expr = subsobj@assays$RNA@data[feature, ]
feature_hs = feature_expr[subsobj$sample_type == "HS"]
feature_hd = feature_expr[subsobj$sample_type == "HD"]
feature_hs_VS_feature_hd = stats::t.test(feature_hs, feature_hd)
feature_hs_VS_feature_hd
# Significance associated with p-value
pval = feature_hs_VS_feature_hd$p.value
significance = case_when(pval > 0.05 ~ "ns",
pval > 0.01 & pval <= 0.05 ~ "*",
pval > 0.001 & pval <= 0.01 ~ "**",
pval <= 0.001 ~ "***")
# Plot
p = Seurat::VlnPlot(subsobj, group.by = "sample_type",
pt.size = 0.3, features = feature) +
ggplot2::scale_fill_manual(values = sample_type_colors,
breaks = names(sample_type_colors)) +
# Significance bar
ggplot2::geom_segment(data = data.frame(1), inherit.aes = FALSE,
size = 0.5,
x = 1, xend = 2,
y = max(feature_expr)+0.3, yend = max(feature_expr)+0.3) +
ggplot2::geom_label(data = data.frame(1), inherit.aes = FALSE,
x = 1.5, y = max(feature_expr)+0.35,
label = significance,
fill = NA, label.size = 0) +
ggplot2::lims(y = c(0, max(feature_expr)+0.4)) +
# Theme
ggplot2::theme(axis.title.x = element_blank(),
legend.position = "none")
return(p)
})
names(plot_list) = features_oi
plot_list## $DUSP1
##
## $DDIT4
##
## $GABARAP
##
## $ZNF302
##
## $MIF
##
## $LGALS7
##
## $ARF5
##
## $S100A9
- violin plots of DE genes in IFE basal, between HS and HD
subsobj = subset(sobj, cell_type == "IFE basal")
features_oi = c("DUSP1", "KLF6", "CLDN1", "CTGF",
"IFI27", "IFITM3", "CCL2", "S100A9")
# Plot !
plot_list = lapply(features_oi, FUN = function(feature) {
# t-test between sample type
feature_expr = subsobj@assays$RNA@data[feature, ]
feature_hs = feature_expr[subsobj$sample_type == "HS"]
feature_hd = feature_expr[subsobj$sample_type == "HD"]
feature_hs_VS_feature_hd = stats::t.test(feature_hs, feature_hd)
feature_hs_VS_feature_hd
# Significance associated with p-value
pval = feature_hs_VS_feature_hd$p.value
significance = case_when(pval > 0.05 ~ "ns",
pval > 0.01 & pval <= 0.05 ~ "*",
pval > 0.001 & pval <= 0.01 ~ "**",
pval <= 0.001 ~ "***")
# Plot
p = Seurat::VlnPlot(subsobj, group.by = "sample_type",
pt.size = 0.3, features = feature) +
ggplot2::scale_fill_manual(values = sample_type_colors,
breaks = names(sample_type_colors)) +
# Significance bar
ggplot2::geom_segment(data = data.frame(1), inherit.aes = FALSE,
size = 0.5,
x = 1, xend = 2,
y = max(feature_expr)+0.3, yend = max(feature_expr)+0.3) +
ggplot2::geom_label(data = data.frame(1), inherit.aes = FALSE,
x = 1.5, y = max(feature_expr)+0.35,
label = significance,
fill = NA, label.size = 0) +
ggplot2::lims(y = c(0, max(feature_expr)+0.4)) +
# Theme
ggplot2::theme(axis.title.x = element_blank(),
legend.position = "none")
return(p)
})
names(plot_list) = features_oi
plot_list## $DUSP1
##
## $KLF6
##
## $CLDN1
##
## $CTGF
##
## $IFI27
##
## $IFITM3
##
## $CCL2
##
## $S100A9
Wu dataset
We select the dataset:
sobj_atlas = list_data$wu_atlas$sobj
name2D_atlas = list_data$wu_atlas$name2D
sobj = list_data$wu_ors_ifeb$sobj
name2D = list_data$wu_ors_ifeb$name2D
list_results = list_data$wu_ors_ifeb$list_resultsLocation of cells on the whole atlas:
sobj_atlas$cell_bc = colnames(sobj_atlas)
sobj$cell_bc = colnames(sobj)
sobj_atlas$is_of_interest = dplyr::left_join(sobj_atlas@meta.data[, c("cell_bc", "percent.mt")],
sobj@meta.data[, c("cell_bc", "cell_type")],
by = "cell_bc")[, "cell_type"]
Seurat::DimPlot(sobj_atlas, reduction = name2D_atlas, pt.size = 0.000001,
group.by = "is_of_interest", order = "TRUE") +
ggplot2::scale_color_manual(values = c(color_markers[cell_type_of_interest], bg_color),
breaks = c(cell_type_of_interest, NA), na.value = bg_color) +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_blank()) +
Seurat::NoAxes() + Seurat::NoLegend()
- GSEA, enriched in ORS compared to IFE basal
the_gs_name = "REACTOME_KERATINIZATION"
the_content = list_results$ORS_vs_IFE_basal$gsea@result %>%
dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
"\t|\tpvalue : ", round(the_content$pvalue, 4),
"\t|\tset size : ", the_content$setSize, " genes")
enrichplot::gseaplot2(x = list_results$ORS_vs_IFE_basal$gsea,
geneSetID = the_gs_name) +
ggplot2::labs(title = the_gs_name,
subtitle = the_subtitle) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
margin = ggplot2::margin(3, 3, 5, 3)),
plot.subtitle = ggtext::element_markdown(hjust = 0.5,
size = 10))
- GSEA, enriched in IFE basal compared to ORS
the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE"
the_content = list_results$ORS_vs_IFE_basal$gsea@result %>%
dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
"\t|\tpvalue : ", round(the_content$pvalue, 4),
"\t|\tset size : ", the_content$setSize, " genes")
enrichplot::gseaplot2(x = list_results$ORS_vs_IFE_basal$gsea,
geneSetID = the_gs_name) +
ggplot2::labs(title = the_gs_name,
subtitle = the_subtitle) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
margin = ggplot2::margin(3, 3, 5, 3)),
plot.subtitle = ggtext::element_markdown(hjust = 0.5,
size = 10))
Takahashi dataset
We select the dataset:
sobj_atlas = list_data$takahashi_atlas$sobj
name2D_atlas = list_data$takahashi_atlas$name2D
sobj = list_data$takahashi_ors_ifeb$sobj
name2D = list_data$takahashi_ors_ifeb$name2D
list_results = list_data$takahashi_ors_ifeb$list_resultsLocation of cells on the whole atlas:
sobj_atlas$cell_bc = colnames(sobj_atlas)
sobj$cell_bc = colnames(sobj)
sobj_atlas$is_of_interest = dplyr::left_join(sobj_atlas@meta.data[, c("cell_bc", "percent.mt")],
sobj@meta.data[, c("cell_bc", "cell_type")],
by = "cell_bc")[, "cell_type"]
Seurat::DimPlot(sobj_atlas, reduction = name2D_atlas, pt.size = 0.000001,
group.by = "is_of_interest", order = "TRUE") +
ggplot2::scale_color_manual(values = c(color_markers[cell_type_of_interest], bg_color),
breaks = c(cell_type_of_interest, NA), na.value = bg_color) +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_blank()) +
Seurat::NoAxes() + Seurat::NoLegend()
- GSEA, enriched in ORS compared to IFE basal
the_gs_name = "REACTOME_KERATINIZATION"
the_content = list_results$ORS_vs_IFE_basal$gsea@result %>%
dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
"\t|\tpvalue : ", round(the_content$pvalue, 4),
"\t|\tset size : ", the_content$setSize, " genes")
enrichplot::gseaplot2(x = list_results$ORS_vs_IFE_basal$gsea,
geneSetID = the_gs_name) +
ggplot2::labs(title = the_gs_name,
subtitle = the_subtitle) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
margin = ggplot2::margin(3, 3, 5, 3)),
plot.subtitle = ggtext::element_markdown(hjust = 0.5,
size = 10))
- GSEA, enriched in IFE basal compared to ORS
the_gs_name = "HALLMARK_INTERFERON_GAMMA_RESPONSE"
the_content = list_results$ORS_vs_IFE_basal$gsea@result %>%
dplyr::filter(ID == the_gs_name)
the_subtitle = paste0("\nNES : ", round(the_content$NES, 2),
"\t|\tpvalue : ", round(the_content$pvalue, 4),
"\t|\tset size : ", the_content$setSize, " genes")
enrichplot::gseaplot2(x = list_results$ORS_vs_IFE_basal$gsea,
geneSetID = the_gs_name) +
ggplot2::labs(title = the_gs_name,
subtitle = the_subtitle) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold",
margin = ggplot2::margin(3, 3, 5, 3)),
plot.subtitle = ggtext::element_markdown(hjust = 0.5,
size = 10))
HF-SCs
We select the dataset:
sobj = list_data$hfsc$sobj
name2D = list_data$hfsc$name2D
list_results = list_data$hfsc$list_results- violin plots of DE genes in HF-SCs, between HS and HD
features_oi = c("HLA-C", "HLA-A", "IFITM3", "MIF", "JUNB", "PPIA",
"PDK4", "DDIT4", "BTG2", "TXNIP", "KLF9")
# Plot !
plot_list = lapply(features_oi, FUN = function(feature) {
# t-test between sample type
feature_expr = sobj@assays$RNA@data[feature, ]
feature_hs = feature_expr[sobj$sample_type == "HS"]
feature_hd = feature_expr[sobj$sample_type == "HD"]
feature_hs_VS_feature_hd = stats::t.test(feature_hs, feature_hd)
feature_hs_VS_feature_hd
# Significance associated with p-value
pval = feature_hs_VS_feature_hd$p.value
significance = case_when(pval > 0.05 ~ "ns",
pval > 0.01 & pval <= 0.05 ~ "*",
pval > 0.001 & pval <= 0.01 ~ "**",
pval <= 0.001 ~ "***")
# Plot
p = Seurat::VlnPlot(sobj, group.by = "sample_type",
pt.size = 0.3, features = feature) +
ggplot2::scale_fill_manual(values = sample_type_colors,
breaks = names(sample_type_colors)) +
# Significance bar
ggplot2::geom_segment(data = data.frame(1), inherit.aes = FALSE,
size = 0.5,
x = 1, xend = 2,
y = max(feature_expr)+0.3, yend = max(feature_expr)+0.3) +
ggplot2::geom_label(data = data.frame(1), inherit.aes = FALSE,
x = 1.5, y = max(feature_expr)+0.35,
label = significance,
fill = NA, label.size = 0) +
ggplot2::lims(y = c(0, max(feature_expr)+0.4)) +
# Theme
ggplot2::theme(axis.title.x = element_blank(),
legend.position = "none")
return(p)
})
names(plot_list) = features_oi
plot_list## $`HLA-C`
##
## $`HLA-A`
##
## $IFITM3
##
## $MIF
##
## $JUNB
##
## $PPIA
##
## $PDK4
##
## $DDIT4
##
## $BTG2
##
## $TXNIP
##
## $KLF9
* heatmap
Heatmap between HS and HD :
# features_oi = list_results$HS_vs_HD %>%
# dplyr::filter(pct.1 > 0.5 | pct.2 > 0.5) %>%
# dplyr::arrange(-avg_logFC, -pct.1, -pct.2) %>%
# rownames()
# features_oi = features_oi[!grepl(features_oi, pattern = "^RP")]
features_oi = c("IFITM3", "MIF", "HLA-C", "LMCD1", "TGFB2", "CYP1B1",
"KRT6B", "WIF1", "HSPA1A", "COMT", "CISD1", "PRNP", "CD81",
"HLA-A", "B2M", "PPIA", "PYCARD", "CSAD", "PFN1", "HIF1A",
"BASP1", "CAV1", "ZNF302", "LMO4", "SEPT11", "HTRA1",
"GPM6A", "SOX4", "ZFP36L2", "RHOB", "CLDN1", "DUSP1", "TBX1",
"ATF3", "DDIT4", "TXNIP", "BTG2", "SGK1", "KLF6", "LGR5")
# Matrix
mat_expr = Seurat::GetAssayData(sobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = cbind(mat_expr, sobj$percent.mt)
colnames(mat_expr)[ncol(mat_expr)] = "percent.rb"
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells## [1] 41 1454## Colors
list_colors = list()
# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
sample_info$color)
# list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(sobj$seurat_clusters))),
# nm = levels(sobj$seurat_clusters))
# Cells order
column_order = sobj@meta.data %>%
dplyr::arrange(sample_type, sample_identifier) %>%
rownames()
column_order = match(column_order, rownames(sobj@meta.data))
# Heatmap
ha_top = HeatmapAnnotation(sample_type = sobj$sample_type,
sample_identifier = sobj$sample_identifier,
# clusters = sobj$seurat_clusters,
col = list(sample_type = list_colors[["sample_type"]],
sample_identifier = list_colors[["sample_identifier"]]
# clusters = list_colors[["seurat_clusters"]]
))
# g1 : REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM
# g2 : GOBP_APOPTOTIC_PROCESS
g1_genes = c("B2M", "HLA-C", "HLA-A", "MIF", "PPIA", "JUNB", "IFITM3")
g2_genes = c("JUN", "ATF3", "BTG2", "RHOB", "NFKBIA", "SGK1", "KLF9",
"CAV1", "DDIT4", "PDK4", "TXNIP", "RNF1152", "TLE1")
ha_right = data.frame(genes = c(features_oi, "percent.rb"), rownames = c(features_oi, "percent.rb"))
ha_right$group = case_when(ha_right$genes %in% g1_genes ~ "REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM",
ha_right$genes %in% g2_genes ~ "GOBP_APOPTOTIC_PROCESS",
TRUE ~ "others")
list_colors[["group"]] = setNames(
nm = c("REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM", "GOBP_APOPTOTIC_PROCESS", "others"),
c("red", "black", "gray90"))
ha_right = HeatmapAnnotation(group = ha_right$group,
col = list(group = list_colors[["group"]]),
which = "row",
show_annotation_name = FALSE,
show_legend = TRUE)
# Heatmap
ht = Heatmap(as.matrix(mat_expr),
heatmap_legend_param = list(title = "Expression", at = c(0, 1),
labels = c("low", "high")),
col = list_colors[["expression"]],
top_annotation = ha_top,
right_annotation = ha_right,
show_column_names = FALSE,
column_order = column_order,
column_gap = unit(2, "mm"),
cluster_rows = FALSE,
show_row_dend = FALSE,
row_title = NULL,
row_names_gp = grid::gpar(fontsize = 10, fontface = "plain"),
use_raster = FALSE,
show_heatmap_legend = TRUE,
border = TRUE)
ComplexHeatmap::draw(ht,
merge_legend = TRUE,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Immune cells
We select the datasets:
sobj_atlas = list_data$our_atlas$sobj
name2D_atlas = list_data$our_atlas$name2D
sobj = list_data$immune$sobj
name2D = list_data$immune$name2D
cell_type_of_interest = c("CD4 T cells", "CD8 T cells", "Langerhans cells",
"macrophages", "B cells", "proliferative")Location of cells on the whole atlas:
sobj_atlas$cell_bc = colnames(sobj_atlas)
sobj$cell_bc = colnames(sobj)
sobj_atlas$is_of_interest = dplyr::left_join(sobj_atlas@meta.data[, c("cell_bc", "percent.mt")],
sobj@meta.data[, c("cell_bc", "cluster_type")],
by = "cell_bc")[, "cluster_type"]
Seurat::DimPlot(sobj_atlas, reduction = name2D_atlas, pt.size = 0.000001,
group.by = "is_of_interest", order = "TRUE") +
ggplot2::scale_color_manual(values = c(color_markers[cell_type_of_interest], bg_color),
breaks = c(cell_type_of_interest, NA), na.value = bg_color) +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_blank()) +
Seurat::NoAxes() + Seurat::NoLegend()
- cell type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cell_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- cluster type annotation
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
group.by = "cluster_type", cols = color_markers) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
- feature plots for genes related to TCR alpha
features_oi = c("CD3E", "CD4", "CD8A",
sort(grep(rownames(sobj), pattern = "^TRA[C|V]", value = TRUE)))
plot_list = lapply(features_oi, FUN = function(one_gene) {
p = Seurat::FeaturePlot(sobj, features = one_gene,
reduction = name2D, order = TRUE) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(aspect.ratio = 1) +
Seurat::NoAxes() +
Seurat::NoLegend()
return(p)
})
patchwork::wrap_plots(plot_list, nrow = 4)
- feature plots split by sample type (HS / HD)
features_oi = data.frame(population = c("all immune cells", rep("macrophages", 2),
rep("CD4 T cells", 3), rep("CD8 T cells", 2)),
feature = c("IL6", "IL1B", "TNF", "GZMA", "IFNG", "IL17A", "PRF1", "GZMB"))
features_oi## population feature
## 1 all immune cells IL6
## 2 macrophages IL1B
## 3 macrophages TNF
## 4 CD4 T cells GZMA
## 5 CD4 T cells IFNG
## 6 CD4 T cells IL17A
## 7 CD8 T cells PRF1
## 8 CD8 T cells GZMBpatchwork_list = lapply(features_oi$feature, FUN = function(one_gene) {
plot_list = aquarius::plot_split_dimred(sobj,
reduction = name2D,
split_by = "sample_type",
color_by = one_gene,
order = TRUE,
bg_color = bg_color)
p = patchwork::wrap_plots(plot_list, nrow = 1) +
patchwork::plot_layout(guides = "collect")
return(p)
})
names(patchwork_list) = features_oi$feature
patchwork_list## $IL6
##
## $IL1B
##
## $TNF
##
## $GZMA
##
## $IFNG
##
## $IL17A
##
## $PRF1
##
## $GZMB
- violin plots within a specific population
plot_list = lapply(c(1:nrow(features_oi)), FUN = function(i) {
one_gene = features_oi$feature[i]
population = features_oi$population[i]
# Subset object
if (population != "all immune cells") {
subsobj = subset(sobj, cluster_type %in% population)
} else {
subsobj = sobj
}
# t-test between sample type
feature_expr = subsobj@assays$RNA@data[one_gene, ]
feature_hs = feature_expr[subsobj$sample_type == "HS"]
feature_hd = feature_expr[subsobj$sample_type == "HD"]
feature_hs_VS_feature_hd = stats::t.test(feature_hs, feature_hd)
feature_hs_VS_feature_hd
# Significance associated with p-value
pval = feature_hs_VS_feature_hd$p.value
significance = case_when(pval > 0.05 ~ "ns",
pval > 0.01 & pval <= 0.05 ~ "*",
pval > 0.001 & pval <= 0.01 ~ "**",
pval <= 0.001 ~ "***")
# Plot
p = Seurat::VlnPlot(subsobj, group.by = "sample_type",
pt.size = 0.3, features = one_gene) +
ggplot2::scale_fill_manual(values = sample_type_colors,
breaks = names(sample_type_colors)) +
ggplot2::labs(title = population,
subtitle = one_gene) +
# Significance bar
ggplot2::geom_segment(data = data.frame(1), inherit.aes = FALSE,
size = 0.5,
x = 1, xend = 2,
y = max(feature_expr)+0.3, yend = max(feature_expr)+0.3) +
ggplot2::geom_label(data = data.frame(1), inherit.aes = FALSE,
x = 1.5, y = max(feature_expr)+0.35,
label = significance,
fill = NA, label.size = 0) +
ggplot2::lims(y = c(0, max(feature_expr)+0.4)) +
# Theme
ggplot2::theme(axis.title.x = element_blank(),
plot.title = element_text(size = 10),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "none")
return(p)
})
names(plot_list) = features_oi$feature
plot_list## $IL6
##
## $IL1B
##
## $TNF
##
## $GZMA
##
## $IFNG
##
## $IL17A
##
## $PRF1
##
## $GZMB
- heatmap macrophages
subsobj = subset(sobj, cluster_type == "macrophages")
features_oi = c("IL1B", "TNF",
"HLA-DQA2", "HLA-DPA1", "HLA-DRB5",
"HLA-A", "HLA-C", "B2M",
"C1QA", "C1QB", "C1QC")
# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells## [1] 11 463## Colors
list_colors = list()
# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
dplyr::arrange(sample_type, sample_identifier) %>%
rownames()
column_order = match(column_order, rownames(subsobj@meta.data))
# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
sample_identifier = subsobj$sample_identifier,
col = list(sample_type = list_colors[["sample_type"]],
sample_identifier = list_colors[["sample_identifier"]]))
# Heatmap
ht = Heatmap(as.matrix(mat_expr),
heatmap_legend_param = list(title = "Expression", at = c(0, 1),
labels = c("low", "high")),
col = list_colors[["expression"]],
top_annotation = ha_top,
show_column_names = FALSE,
column_order = column_order,
column_gap = unit(2, "mm"),
cluster_rows = FALSE,
row_title = NULL,
row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
use_raster = FALSE,
show_heatmap_legend = TRUE,
border = TRUE)
ComplexHeatmap::draw(ht,
merge_legend = TRUE,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
- heatmap T cells
subsobj = subset(sobj, cluster_type == "CD4 T cells")
features_oi = c("GZMA", "KLRB1", "BTG1", "ZFP36", "NFKBIA", "TXNIP", "CXCR4", "IFNG", "IL17A")
# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells## [1] 9 848## Colors
list_colors = list()
# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
dplyr::arrange(sample_type, sample_identifier) %>%
rownames()
column_order = match(column_order, rownames(subsobj@meta.data))
# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
sample_identifier = subsobj$sample_identifier,
col = list(sample_type = list_colors[["sample_type"]],
sample_identifier = list_colors[["sample_identifier"]]))
# Heatmap
ht = Heatmap(as.matrix(mat_expr),
heatmap_legend_param = list(title = "Expression", at = c(0, 1),
labels = c("low", "high")),
col = list_colors[["expression"]],
top_annotation = ha_top,
show_column_names = FALSE,
column_order = column_order,
column_gap = unit(2, "mm"),
cluster_rows = FALSE,
row_title = NULL,
row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
use_raster = FALSE,
show_heatmap_legend = TRUE,
border = TRUE)
ComplexHeatmap::draw(ht,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")
Supplementary tables
In this section, we save files to associate with the manuscript, as supplementary tables.
Table S2 (package version)
We load the table :
package_version = read.table(paste0(".", "/data/info_to_install_2023_04_17.txt"),
header = TRUE)
head(package_version)## order package_name version
## 1 1 acepack 1.4.1
## 2 2 ADGofTest 0.3
## 3 3 backports 1.2.1
## 4 4 base64enc 0.1-3
## 5 5 BH 1.72.0-3
## 6 6 bit 4.0.4
## url
## 1 https://cran.r-project.org/src/contrib/acepack_1.4.1.tar.gz
## 2 https://cran.r-project.org/src/contrib/ADGofTest_0.3.tar.gz
## 3 http://cran.r-project.org/src/contrib/Archive/backports/backports_1.2.1.tar.gz
## 4 https://cran.r-project.org/src/contrib/base64enc_0.1-3.tar.gz
## 5 http://cran.r-project.org/src/contrib/Archive/BH/BH_1.72.0-3.tar.gz
## 6 http://cran.r-project.org/src/contrib/Archive/bit/bit_4.0.4.tar.gz
## type
## 1 CRAN_last
## 2 CRAN_last
## 3 CRAN_archive
## 4 CRAN_last
## 5 CRAN_archive
## 6 CRAN_archiveColumns correspond to :
- order : order to install package such that one never need to install dependencies
- package_name : package name
- version : installed version, on the local machine
- url : the package was installed in the Singularity container using this url
We save this table, except the last column (just for me) :
openxlsx::write.xlsx(package_version[, c(1:4)],
file = paste0(".", "/data/Supplementary Table 2.xlsx"))Table S3 (cell type annotation)
We load the table :
cell_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_cell_markers.rds"))
lengths(cell_markers)## CD4 T cells CD8 T cells Langerhans cells
## 13 13 9
## macrophages B cells cuticle
## 10 16 15
## cortex medulla IRS
## 16 10 16
## proliferative HF-SCs IFE basal
## 20 17 16
## IFE granular spinous ORS melanocytes
## 17 15 10
## sebocytes
## 8We save this table, except the last column (just for me) :
openxlsx::write.xlsx(cell_markers,
file = paste0(".", "/data/Supplementary Table 3.xlsx"))R Session
show
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
##
## locale:
## [1] C
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ComplexHeatmap_2.14.0 ggplot2_3.3.5 patchwork_1.1.2
## [4] dplyr_1.0.7
##
## loaded via a namespace (and not attached):
## [1] softImpute_1.4 graphlayouts_0.7.0
## [3] pbapply_1.4-2 lattice_0.20-41
## [5] haven_2.3.1 dyndimred_1.0.3
## [7] vctrs_0.3.8 usethis_2.0.1
## [9] dynwrap_1.2.1 blob_1.2.1
## [11] survival_3.2-13 prodlim_2019.11.13
## [13] dynutils_1.0.5 later_1.3.0
## [15] DBI_1.1.1 R.utils_2.11.0
## [17] SingleCellExperiment_1.8.0 rappdirs_0.3.3
## [19] uwot_0.1.8 dqrng_0.2.1
## [21] jpeg_0.1-8.1 zlibbioc_1.32.0
## [23] pspline_1.0-18 pcaMethods_1.78.0
## [25] mvtnorm_1.1-1 htmlwidgets_1.5.4
## [27] GlobalOptions_0.1.2 future_1.22.1
## [29] UpSetR_1.4.0 laeken_0.5.2
## [31] leiden_0.3.3 clustree_0.4.3
## [33] lmds_0.1.0 parallel_3.6.3
## [35] scater_1.14.6 irlba_2.3.3
## [37] markdown_1.1 DEoptimR_1.0-9
## [39] tidygraph_1.1.2 Rcpp_1.0.9
## [41] readr_2.0.2 KernSmooth_2.23-17
## [43] carrier_0.1.0 promises_1.1.0
## [45] gdata_2.18.0 DelayedArray_0.12.3
## [47] limma_3.42.2 graph_1.64.0
## [49] RcppParallel_5.1.4 Hmisc_4.4-0
## [51] fs_1.5.2 RSpectra_0.16-0
## [53] fastmatch_1.1-0 ranger_0.12.1
## [55] digest_0.6.25 png_0.1-7
## [57] sctransform_0.2.1 cowplot_1.0.0
## [59] DOSE_3.12.0 here_1.0.1
## [61] TInGa_0.0.0.9000 dynplot_1.1.0
## [63] ggraph_2.0.3 pkgconfig_2.0.3
## [65] GO.db_3.10.0 DelayedMatrixStats_1.8.0
## [67] gower_0.2.1 ggbeeswarm_0.6.0
## [69] iterators_1.0.12 DropletUtils_1.6.1
## [71] reticulate_1.26 clusterProfiler_3.14.3
## [73] SummarizedExperiment_1.16.1 circlize_0.4.15
## [75] beeswarm_0.4.0 GetoptLong_1.0.5
## [77] xfun_0.35 bslib_0.3.1
## [79] zoo_1.8-10 tidyselect_1.1.0
## [81] GA_3.2 reshape2_1.4.4
## [83] purrr_0.3.4 ica_1.0-2
## [85] pcaPP_1.9-73 viridisLite_0.3.0
## [87] rtracklayer_1.46.0 rlang_1.0.2
## [89] hexbin_1.28.1 jquerylib_0.1.4
## [91] dyneval_0.9.9 glue_1.4.2
## [93] waldo_0.3.1 RColorBrewer_1.1-2
## [95] matrixStats_0.56.0 stringr_1.4.0
## [97] lava_1.6.7 europepmc_0.3
## [99] DESeq2_1.26.0 recipes_0.1.17
## [101] labeling_0.3 httpuv_1.5.2
## [103] class_7.3-17 BiocNeighbors_1.4.2
## [105] DO.db_2.9 annotate_1.64.0
## [107] jsonlite_1.7.2 XVector_0.26.0
## [109] bit_4.0.4 mime_0.9
## [111] aquarius_0.1.5 Rsamtools_2.2.3
## [113] gridExtra_2.3 gplots_3.0.3
## [115] stringi_1.4.6 processx_3.5.2
## [117] gsl_2.1-6 bitops_1.0-6
## [119] cli_3.0.1 batchelor_1.2.4
## [121] RSQLite_2.2.0 randomForest_4.6-14
## [123] tidyr_1.1.4 data.table_1.14.2
## [125] rstudioapi_0.13 org.Mm.eg.db_3.10.0
## [127] GenomicAlignments_1.22.1 nlme_3.1-147
## [129] qvalue_2.18.0 scran_1.14.6
## [131] locfit_1.5-9.4 scDblFinder_1.1.8
## [133] listenv_0.8.0 ggthemes_4.2.4
## [135] gridGraphics_0.5-0 R.oo_1.24.0
## [137] dbplyr_1.4.4 BiocGenerics_0.32.0
## [139] TTR_0.24.2 readxl_1.3.1
## [141] lifecycle_1.0.1 timeDate_3043.102
## [143] ggpattern_0.3.1 munsell_0.5.0
## [145] cellranger_1.1.0 R.methodsS3_1.8.1
## [147] proxyC_0.1.5 visNetwork_2.0.9
## [149] caTools_1.18.0 codetools_0.2-16
## [151] Biobase_2.46.0 GenomeInfoDb_1.22.1
## [153] vipor_0.4.5 lmtest_0.9-38
## [155] msigdbr_7.5.1 htmlTable_1.13.3
## [157] triebeard_0.3.0 lsei_1.2-0
## [159] xtable_1.8-4 ROCR_1.0-7
## [161] BiocManager_1.30.10 scatterplot3d_0.3-41
## [163] abind_1.4-5 farver_2.0.3
## [165] parallelly_1.28.1 RANN_2.6.1
## [167] askpass_1.1 GenomicRanges_1.38.0
## [169] RcppAnnoy_0.0.16 tibble_3.1.5
## [171] ggdendro_0.1-20 cluster_2.1.0
## [173] future.apply_1.5.0 Seurat_3.1.5
## [175] dendextend_1.15.1 Matrix_1.3-2
## [177] ellipsis_0.3.2 prettyunits_1.1.1
## [179] lubridate_1.7.9 ggridges_0.5.2
## [181] igraph_1.2.5 RcppEigen_0.3.3.7.0
## [183] fgsea_1.12.0 remotes_2.4.2
## [185] scBFA_1.0.0 destiny_3.0.1
## [187] VIM_6.1.1 testthat_3.1.0
## [189] htmltools_0.5.2 BiocFileCache_1.10.2
## [191] yaml_2.2.1 utf8_1.1.4
## [193] plotly_4.9.2.1 XML_3.99-0.3
## [195] ModelMetrics_1.2.2.2 e1071_1.7-3
## [197] foreign_0.8-76 withr_2.5.0
## [199] fitdistrplus_1.0-14 BiocParallel_1.20.1
## [201] xgboost_1.4.1.1 bit64_4.0.5
## [203] foreach_1.5.0 robustbase_0.93-9
## [205] Biostrings_2.54.0 GOSemSim_2.13.1
## [207] rsvd_1.0.3 memoise_2.0.0
## [209] evaluate_0.18 forcats_0.5.0
## [211] rio_0.5.16 geneplotter_1.64.0
## [213] tzdb_0.1.2 caret_6.0-86
## [215] ps_1.6.0 DiagrammeR_1.0.6.1
## [217] curl_4.3 fdrtool_1.2.15
## [219] fansi_0.4.1 highr_0.8
## [221] urltools_1.7.3 xts_0.12.1
## [223] GSEABase_1.48.0 acepack_1.4.1
## [225] edgeR_3.28.1 checkmate_2.0.0
## [227] scds_1.2.0 cachem_1.0.6
## [229] npsurv_0.4-0 babelgene_22.3
## [231] rjson_0.2.20 openxlsx_4.1.5
## [233] ggrepel_0.9.1 clue_0.3-60
## [235] rprojroot_2.0.2 stabledist_0.7-1
## [237] tools_3.6.3 sass_0.4.0
## [239] nichenetr_1.1.1 magrittr_2.0.1
## [241] RCurl_1.98-1.2 proxy_0.4-24
## [243] car_3.0-11 ape_5.3
## [245] ggplotify_0.0.5 xml2_1.3.2
## [247] httr_1.4.2 assertthat_0.2.1
## [249] rmarkdown_2.18 boot_1.3-25
## [251] globals_0.14.0 R6_2.4.1
## [253] Rhdf5lib_1.8.0 nnet_7.3-14
## [255] RcppHNSW_0.2.0 progress_1.2.2
## [257] genefilter_1.68.0 statmod_1.4.34
## [259] gtools_3.8.2 shape_1.4.6
## [261] HDF5Array_1.14.4 BiocSingular_1.2.2
## [263] rhdf5_2.30.1 splines_3.6.3
## [265] AUCell_1.8.0 carData_3.0-4
## [267] colorspace_1.4-1 generics_0.1.0
## [269] stats4_3.6.3 base64enc_0.1-3
## [271] dynfeature_1.0.0 smoother_1.1
## [273] gridtext_0.1.1 pillar_1.6.3
## [275] tweenr_1.0.1 sp_1.4-1
## [277] ggplot.multistats_1.0.0 rvcheck_0.1.8
## [279] GenomeInfoDbData_1.2.2 plyr_1.8.6
## [281] gtable_0.3.0 zip_2.2.0
## [283] knitr_1.41 latticeExtra_0.6-29
## [285] biomaRt_2.42.1 IRanges_2.20.2
## [287] fastmap_1.1.0 ADGofTest_0.3
## [289] copula_1.0-0 doParallel_1.0.15
## [291] AnnotationDbi_1.48.0 vcd_1.4-8
## [293] babelwhale_1.0.1 openssl_1.4.1
## [295] scales_1.1.1 backports_1.2.1
## [297] S4Vectors_0.24.4 ipred_0.9-12
## [299] enrichplot_1.6.1 hms_1.1.1
## [301] ggforce_0.3.1 Rtsne_0.15
## [303] shiny_1.7.1 numDeriv_2016.8-1.1
## [305] polyclip_1.10-0 lazyeval_0.2.2
## [307] Formula_1.2-3 tsne_0.1-3
## [309] crayon_1.3.4 MASS_7.3-54
## [311] pROC_1.16.2 viridis_0.5.1
## [313] dynparam_1.0.0 rpart_4.1-15
## [315] zinbwave_1.8.0 compiler_3.6.3
## [317] 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ame of the 2D projection to visualize cells on
* various third element such as: sample information, trajectory, results from gene analysis...

```{r list_data}
list_data = list()

## Our dataset
# Atlas of all cells
list_data$our_atlas$sobj = readRDS(paste0(data_dir, "/3_combined/hs_hd_sobj.rds"))
list_data$our_atlas$name2D = "harmony_38_tsne"
list_data$our_atlas$sample_info = readRDS(paste0(data_dir, "/1_metadata/hs_hd_sample_info.rds"))
list_data$our_atlas$sobj

# ORS + IFEb dataset
list_data$ors_ifeb$sobj = readRDS(paste0(data_dir, "/4_zoom/3_zoom_ors_ifeb/ors_ifeb_sobj.rds"))
list_data$ors_ifeb$name2D = "harmony_20_tsne"
list_data$ors_ifeb$list_results = readRDS(paste0(data_dir, "/4_zoom/3_zoom_ors_ifeb/ors_ifeb_list_results.rds"))
list_data$ors_ifeb$sobj

# HF-SCs dataset
list_data$hfsc$sobj = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_sobj.rds"))
list_data$hfsc$name2D = "harmony_24_tsne"
list_data$hfsc$list_results = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_list_results.rds"))
list_data$hfsc$sobj

# Non matrix cells dataset
list_data$non_matrix$sobj = readRDS(paste0(data_dir, "/4_zoom/5_zoom_non_matrix/non_matrix_sobj_traj_tinga.rds"))
list_data$non_matrix$name2D = "harmony_dm"
list_data$non_matrix$my_traj = readRDS(paste0(data_dir, "/4_zoom/5_zoom_non_matrix/non_matrix_my_traj_tinga.rds"))
list_data$non_matrix$sobj

# Matrix cells dataset (not considered in the manuscript)
# list_data$matrix$sobj = readRDS(paste0(data_dir, "/4_zoom/6_zoom_matrix/matrix_sobj.rds"))
# list_data$matrix$name2D = "harmony_19_tsne"
# list_data$matrix$list_results = readRDS(paste0(data_dir, "/4_zoom/6_zoom_matrix/matrix_list_results.rds"))
# list_data$matrix$sobj

# Immune cells
list_data$immune$sobj = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_sobj.rds"))
list_data$immune$name2D = "harmony_20_tsne"
list_data$immune$list_results = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_list_results.rds"))
list_data$immune$sobj

## Wu dataset (OEP002321)
# Atlas of all cells
list_data$wu_atlas$sobj = readRDS(paste0(data_dir, "/5_wu/3_combined/wu_sobj.rds"))
list_data$wu_atlas$name2D = "harmony_39_tsne"
list_data$wu_atlas$sample_info = readRDS(paste0(data_dir, "/5_wu/1_metadata/wu_sample_info.rds"))

# ORS + IFEb dataset
list_data$wu_ors_ifeb$sobj = readRDS(paste0(data_dir, "/5_wu/4_ors_ifeb/ors_ifeb_sobj.rds"))
list_data$wu_ors_ifeb$name2D = "harmony_20_tsne"
list_data$wu_ors_ifeb$list_results = readRDS(paste0(data_dir, "/5_wu/4_ors_ifeb/ors_ifeb_list_results.rds"))
list_data$wu_ors_ifeb$sobj

## Takahashi dataset (GSE129611)
# Atlas of all cells
list_data$takahashi_atlas$sobj = readRDS(paste0(data_dir, "/6_takahashi/3_combined/takahashi_sobj.rds"))
list_data$takahashi_atlas$name2D = "harmony_41_tsne"
list_data$takahashi_atlas$sample_info = readRDS(paste0(data_dir, "/6_takahashi/1_metadata/takahashi_sample_info.rds"))
list_data$takahashi_atlas$sobj

# ORS + IFEb dataset
list_data$takahashi_ors_ifeb$sobj = readRDS(paste0(data_dir, "/6_takahashi/4_ors_ifeb/ors_ifeb_sobj.rds"))
list_data$takahashi_ors_ifeb$name2D = "harmony_23_tsne"
list_data$takahashi_ors_ifeb$list_results = readRDS(paste0(data_dir, "/6_takahashi/4_ors_ifeb/ors_ifeb_list_results.rds"))
list_data$takahashi_ors_ifeb$sobj

## Three datasets combined (Our + OEP002321 + GSE129611)
list_data$combined$sobj = readRDS(paste0(data_dir, "/3_combined/data3_sobj.rds"))
list_data$combined$name2D = "harmony_37_tsne"
list_data$combined$sobj

## Print
str(list_data, max.level = 2)
```


# Figures

## Sample information

Our dataset:

```{r sample_info_our, fig.width = 12, fig.height = 3}
sobj = list_data$our_atlas$sobj
sample_info = list_data$our_atlas$sample_info

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
```

Wu dataset:

```{r sample_info_wu, fig.width = 12, fig.height = 3}
sobj = list_data$wu_atlas$sobj
sample_info = list_data$wu_atlas$sample_info

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
```

Takahashi dataset:

```{r sample_info_takahashi, fig.width = 12, fig.height = 3}
sobj = list_data$takahashi_atlas$sobj
sample_info = list_data$takahashi_atlas$sample_info

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
```

## Annotation smoothing

For each dataset, we smooth the single-cell level cell type annotation at a cluster level.

* Our dataset:

```{r smoothing_atlas_our}
# Select dataset
sobj = list_data$our_atlas$sobj

# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type))

# Consider change
list_data$our_atlas$sobj = sobj
```

```{r smoothing_non_matrix_our}
# Select dataset
sobj = list_data$non_matrix$sobj

# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type))

# Consider change
list_data$non_matrix$sobj = sobj
```

```{r smoothing_immune_our}
# Select dataset
sobj = list_data$immune$sobj

# Annotation smoothing
sobj$cell_type = sobj$cell_type %>%
  as.character() %>%
  factor(., levels = names(color_markers))

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clus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